Optimizing reliability and cost of system for aggregate production planning in a supply chain

In this paper, the researchers present a multi-objective model for multiproduct, multi-site aggregate production planning model in a supply chain. The goals are to minimize the total cost of the supply chain, including inventory costs, manufacturing costs, work force costs, hiring and firing costs, and also to maximize the minimum of suppliers' reliability by considering probabilistic lead times to simultaneously improve the system performance. Since the problem is NP-Hard, a Pareto-based multi-objective harmony search algorithm is proposed. To demonstrate the performance of the presented algorithm, a Non-dominated Sorting Genetic Algorithm (NSGA-II) and a Non-dominated Ranking Genetic Algorithm (NRGA) are applied. The results demonstrate the robustness of the proposed algorithm to probe the Pareto solutions.